Abstract

This study describes the basic framework of EM algorithm and gives how to apply EM algorithm to solve the problem of maximum-likelihood parameters estimation combining with the models of HMM and PCFG. In the process of statistics natural language, one kind of problem is often encountered that is how to solve the parameter's maximum-likelihood estimation when observation data is incomplete. EM algorithm is the classical method to solve this problem. Finally, the advantages and disadvantages of EM algorithm are discussed.

Keywords:

Context-free grammar, EM algorithm, hidden Markov model, likelihood function, natural language, parameter estimation,

References

Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.